Critical GenAI Literacies

Ethical, Epistemic, and Practical Considerations

Alasdair B R Stewart

Introduction

Note

I have setup custom GPTs to illustrate concepts and use cases.

To use them you only need a free ChatGPT account.

  • All a custom GPT is is a convenient way to save a specific prompt.
  • The instructions for each are available within the online materials.
  • If you prefer to use another genAI tool, just copy&paste the instructions as the opening prompt in a new chat.

Important

It is vital that any use of generative AI complies with the policies and guidance of your academic institution and funder.

What we will cover today is not a replacement to any guidance provided by your institution and/or funder. Instead, it aims to supplement and clarify such guidance with critical reflexive considerations to make when using genAI.

Note

You do not need to pay for genAI tools - and your university will never expect you to pay out of your own pocket for anything required in your programme / courses.

Competition between companies also means most models and core features are available for free - subscriptions usually just buy higher usage limits and some non-essential additional features.

Critical GenAI Literacies

Critical genAI literacies are important for understanding genAI development, impacts, and use-cases.

  • Assumptions and intentions behind genAI development and deployment.
  • Social, political, and economic impacts of genAI.
  • Questions of fairness, justice, and equality.
  • Learning to use genAI critically, ethically, and effectively.
  • Challenging narratives pushed by the AI hype-machine.

An overview of LLM training

Vast amounts of data is gathered from the open web, including copyright content without permission and illegal content.

A ‘base model’ is trained on this data, it can generate text but not necessarily and consistently in a back-and-forth conversation style.

An ‘instruction (aka chat) model’ is made through fine-tuning the initial base model on example prompts and responses.

An ‘aligned model’ is made by further fine-tuning the instrution model and using safe-guard mechanisms to reduce offensive and harmful content in responses.

%%{init: {"themeVariables": {"fontSize":"25px"}}}%%
flowchart TD
    A["Training Data"] --> B["Base Model"]
    B --> C["Instruction-Tuned Model"]
    C --> D["Aligned Model"]
    linkStyle 0 stroke-width:4px 
    linkStyle 1 stroke-width:4px
    linkStyle 2 stroke-width:4px

The training of genaI is not “data + magic algorithm = AI”.

Humans are involved in creating, collecting, labelling, reviewing, and so on.

Humans also heavily involved in design decisions and choices that influence how genAI models respond…

An overview of genAI chat loop

System instructions are pre-setup when using consumer facing chat interfaces such as ChatGPT, Gemini, and Claude.

System instructions are used for mix of alignment, style, and so on, as well as information on tools available to the LLM (e.g. web browsing).

When given a prompt in a new chat, the system instructions and the prompt are passed to the LLM - alongside any data such as file uploads, ‘memories’, etc.

The model predicts one word (or more precisely ‘token’) at a time. After predicting a word, it adds it to the ‘context window’ then predicts the next word from it, and so on.

%%{init: {"themeVariables": {"fontSize":"25px"}}}%%
flowchart TD
  subgraph CW["Context Window"]
    A[System Instructions] --> B[Chat History + Any Data]
  end

 CW --> M[LLM]
 M --> R[Model Response]
 R --> CW

AI Fallacies